Note: Currently going under complete re-development, please come back later

Last Updated: 10/14/16

DataTools is a Python module which provides tools to aid in predictive modeling and machine learning development. DataTools is designed to be generic enough to be applied to any model but contains a specific set of tools I find useful in my role as a data scientist. The purpose of DataTools is to save time and effort writing code to perform the complex tasks below but is not meant to be a standalone modeling package.

• Model Evaluation
  • Classification
  • Regression(like)
  • Automated parameter selection
• Feature Evaluation
  • Automated feature selection
  • Feature variance visualization
  • Interaction
• Feature Creation
  • Time-based transaction data conversion
• Data Manipulation
  • Fill missing values

DataTools provides a CrossValidate function which evaluates a user's model over a range of different features and parameters and provides an easy to read results. Currently this is accomplished through a brute grid search but more intelligent search methods will be added later.

The CrossValidate function can be used on any predictive model but the model must be passed with a "wrapper object" which contains the methods listed in the example wrapper below.

class wrapper_template(object):

    def __init__(self):
    
        #|Set to either 'classification' or 'regression' based
        #|on model type (will determine evaluation functions)
        self.mod_type = 'classification' 
        
        self.features = ['var_a','var_b','var_c']
        
        self.parameters = {'A':[1,5,10], 'B':['dog','cat']}
        
    def fit(self, feat, param, index):
    
        return model
        
    def predict(self, feat, param, index):
    
        return prediction, actual